By solving Eq.(REF ) we find that \(Re_{rt}\)  = 14.06 is its only root. That makes \(Re_{rt}\)  the only \(Re\)  value that zeroes off all terms beyond the constant \(a_1\) . This \(Re_{rt}\)  is close to values of \(Re_s\)  reported in literature. For example, the relative error with respect to the results of Bourot[1]} and Chang and Maxey [2]} is 8% and 30%, respectively. We conjecture that \(Re_{rt}\)  is representing \(Re_s\) , even though we do not have any proof for this. We believe we are witnessing an instance where the machine learning algorithm found a mathematical description of a physical phenomenon, which needs human abilities to be interpreted in terms of physical laws. Otherwise, it will be a good approximation, that can describe some of the physics involved in the process of flow separation. As far as the authors are aware, there is only one analytical prediction for the point of first flow separation, from slow motion viscous theory [3]}, [4]}. However, that result was disputed by the authors of [3]}, [4]}, as we will show later. In practice, we depend on numerical simulations to find the point of zero local shear stress, as described by boundary layer theory [7]}. However, Batchelor [8]} raised serious doubts about estimating the onset of separation by this method.
